Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations134766
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.9 MiB
Average record size in memory248.0 B

Variable types

Numeric8
Text1
Unsupported1
DateTime2
Categorical18

Alerts

Avg source IP count is highly overall correlated with IPv4 fragmentationHigh correlation
CHARGEN is highly overall correlated with Data speedHigh correlation
Data speed is highly overall correlated with CHARGEN and 2 other fieldsHigh correlation
Detect count is highly overall correlated with DurationHigh correlation
Duration is highly overall correlated with Detect countHigh correlation
Generic UDP is highly overall correlated with TypeHigh correlation
High volume traffic is highly overall correlated with Suspicious traffic and 1 other fieldsHigh correlation
IPv4 fragmentation is highly overall correlated with Avg source IP count and 1 other fieldsHigh correlation
Packet speed is highly overall correlated with Data speedHigh correlation
SYN Attack is highly overall correlated with TypeHigh correlation
Suspicious traffic is highly overall correlated with High volume traffic and 1 other fieldsHigh correlation
Type is highly overall correlated with Generic UDP and 3 other fieldsHigh correlation
Type is highly imbalanced (72.7%) Imbalance
ACK Attack is highly imbalanced (> 99.9%) Imbalance
CHARGEN is highly imbalanced (> 99.9%) Imbalance
CLDAP is highly imbalanced (99.3%) Imbalance
CoAP is highly imbalanced (99.9%) Imbalance
DNS is highly imbalanced (98.6%) Imbalance
Generic UDP is highly imbalanced (97.6%) Imbalance
High volume traffic is highly imbalanced (69.7%) Imbalance
IPv4 fragmentation is highly imbalanced (99.8%) Imbalance
NTP is highly imbalanced (99.4%) Imbalance
RDP is highly imbalanced (> 99.9%) Imbalance
RPC is highly imbalanced (> 99.9%) Imbalance
SNMP is highly imbalanced (> 99.9%) Imbalance
SSDP is highly imbalanced (99.9%) Imbalance
SYN Attack is highly imbalanced (98.1%) Imbalance
Sentinel is highly imbalanced (99.9%) Imbalance
Suspicious traffic is highly imbalanced (62.2%) Imbalance
TCP Anomaly is highly imbalanced (99.9%) Imbalance
Detect count is highly skewed (γ1 = 117.331734) Skewed
Avg source IP count is highly skewed (γ1 = 98.67006016) Skewed
Duration is highly skewed (γ1 = 70.56919705) Skewed
Attack ID is uniformly distributed Uniform
Attack ID has unique values Unique
Attack code is an unsupported type, check if it needs cleaning or further analysis Unsupported
Port number has 21470 (15.9%) zeros Zeros
Avg packet len has 38076 (28.3%) zeros Zeros
Duration has 19644 (14.6%) zeros Zeros

Reproduction

Analysis started2025-03-09 14:33:41.936109
Analysis finished2025-03-09 14:33:51.582999
Duration9.65 seconds
Software versionydata-profiling vv4.13.0
Download configurationconfig.json

Variables

Attack ID
Real number (ℝ)

Uniform  Unique 

Distinct134766
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67383.5
Minimum1
Maximum134767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-03-09T14:33:51.640755image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6739.25
Q133692.25
median67383.5
Q3101074.75
95-th percentile128027.75
Maximum134767
Range134766
Interquartile range (IQR)67382.5

Descriptive statistics

Standard deviation38903.738
Coefficient of variation (CV)0.57734813
Kurtosis-1.2
Mean67383.5
Median Absolute Deviation (MAD)33691.5
Skewness3.433112 × 10-9
Sum9.0810048 × 109
Variance1.5135008 × 109
MonotonicityStrictly increasing
2025-03-09T14:33:51.733846image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134767 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
Other values (134756) 134756
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
134767 1
< 0.1%
134766 1
< 0.1%
134765 1
< 0.1%
134763 1
< 0.1%
134762 1
< 0.1%
134761 1
< 0.1%
134760 1
< 0.1%
134759 1
< 0.1%
134758 1
< 0.1%
134757 1
< 0.1%
Distinct18200
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2025-03-09T14:33:51.865935image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.151559
Min length7

Characters and Unicode

Total characters963787
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12249 ?
Unique (%)9.1%

Sample

1st rowIP_0001
2nd rowIP_0002
3rd rowIP_0003
4th rowIP_0002
5th rowIP_0004
ValueCountFrequency (%)
ip_0010 11340
 
8.4%
ip_0040 7788
 
5.8%
ip_0024 5592
 
4.1%
ip_0006 4092
 
3.0%
ip_0017 3999
 
3.0%
ip_14488 3245
 
2.4%
ip_0001 3186
 
2.4%
ip_0003 2848
 
2.1%
ip_0018 2689
 
2.0%
ip_0015 2639
 
2.0%
Other values (18190) 87348
64.8%
2025-03-09T14:33:52.067861image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 191814
19.9%
I 134766
14.0%
P 134766
14.0%
_ 134766
14.0%
1 83850
8.7%
4 47956
 
5.0%
2 44224
 
4.6%
5 38660
 
4.0%
3 37024
 
3.8%
6 33954
 
3.5%
Other values (3) 82007
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 963787
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 191814
19.9%
I 134766
14.0%
P 134766
14.0%
_ 134766
14.0%
1 83850
8.7%
4 47956
 
5.0%
2 44224
 
4.6%
5 38660
 
4.0%
3 37024
 
3.8%
6 33954
 
3.5%
Other values (3) 82007
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 963787
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 191814
19.9%
I 134766
14.0%
P 134766
14.0%
_ 134766
14.0%
1 83850
8.7%
4 47956
 
5.0%
2 44224
 
4.6%
5 38660
 
4.0%
3 37024
 
3.8%
6 33954
 
3.5%
Other values (3) 82007
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 963787
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 191814
19.9%
I 134766
14.0%
P 134766
14.0%
_ 134766
14.0%
1 83850
8.7%
4 47956
 
5.0%
2 44224
 
4.6%
5 38660
 
4.0%
3 37024
 
3.8%
6 33954
 
3.5%
Other values (3) 82007
8.5%

Port number
Real number (ℝ)

Zeros 

Distinct29623
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28012.637
Minimum0
Maximum65535
Zeros21470
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-03-09T14:33:52.157955image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1443
median29586
Q354083
95-th percentile62777
Maximum65535
Range65535
Interquartile range (IQR)53640

Descriptive statistics

Standard deviation25876.134
Coefficient of variation (CV)0.92373076
Kurtosis-1.7922974
Mean28012.637
Median Absolute Deviation (MAD)26770
Skewness0.099381825
Sum3.775151 × 109
Variance6.6957434 × 108
MonotonicityNot monotonic
2025-03-09T14:33:52.241424image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21470
 
15.9%
4500 20000
 
14.8%
80 7000
 
5.2%
443 6020
 
4.5%
60645 3700
 
2.7%
51821 2384
 
1.8%
1200 1994
 
1.5%
25 1005
 
0.7%
34863 686
 
0.5%
49261 543
 
0.4%
Other values (29613) 69964
51.9%
ValueCountFrequency (%)
0 21470
15.9%
12 1
 
< 0.1%
20 22
 
< 0.1%
22 351
 
0.3%
23 3
 
< 0.1%
25 1005
 
0.7%
53 182
 
0.1%
68 1
 
< 0.1%
80 7000
 
5.2%
86 1
 
< 0.1%
ValueCountFrequency (%)
65535 5
< 0.1%
65534 1
 
< 0.1%
65533 2
 
< 0.1%
65532 2
 
< 0.1%
65531 5
< 0.1%
65529 1
 
< 0.1%
65528 4
< 0.1%
65527 1
 
< 0.1%
65526 2
 
< 0.1%
65525 8
< 0.1%

Attack code
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size2.1 MiB

Detect count
Real number (ℝ)

High correlation  Skewed 

Distinct461
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3642759
Minimum1
Maximum12534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-03-09T14:33:52.325313image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile8
Maximum12534
Range12533
Interquartile range (IQR)1

Descriptive statistics

Standard deviation55.876602
Coefficient of variation (CV)12.803178
Kurtosis21578.979
Mean4.3642759
Median Absolute Deviation (MAD)0
Skewness117.33173
Sum588156
Variance3122.1947
MonotonicityNot monotonic
2025-03-09T14:33:52.416098image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 86267
64.0%
2 23940
 
17.8%
3 8357
 
6.2%
4 4066
 
3.0%
5 2293
 
1.7%
6 1637
 
1.2%
7 1155
 
0.9%
8 935
 
0.7%
9 787
 
0.6%
10 574
 
0.4%
Other values (451) 4755
 
3.5%
ValueCountFrequency (%)
1 86267
64.0%
2 23940
 
17.8%
3 8357
 
6.2%
4 4066
 
3.0%
5 2293
 
1.7%
6 1637
 
1.2%
7 1155
 
0.9%
8 935
 
0.7%
9 787
 
0.6%
10 574
 
0.4%
ValueCountFrequency (%)
12534 1
< 0.1%
6443 1
< 0.1%
5300 1
< 0.1%
4995 1
< 0.1%
3573 1
< 0.1%
3226 1
< 0.1%
3155 1
< 0.1%
2646 1
< 0.1%
2257 1
< 0.1%
1966 1
< 0.1%

Packet speed
Real number (ℝ)

High correlation 

Distinct16241
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75364.633
Minimum10500
Maximum3526021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-03-09T14:33:52.501841image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum10500
5-th percentile51500
Q157200
median65057.5
Q377500
95-th percentile153500
Maximum3526021
Range3515521
Interquartile range (IQR)20300

Descriptive statistics

Standard deviation40115.643
Coefficient of variation (CV)0.53228738
Kurtosis608.59213
Mean75364.633
Median Absolute Deviation (MAD)9257.5
Skewness12.486469
Sum1.015659 × 1010
Variance1.6092648 × 109
MonotonicityNot monotonic
2025-03-09T14:33:52.591920image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54000 504
 
0.4%
51700 491
 
0.4%
51600 487
 
0.4%
52800 476
 
0.4%
51200 471
 
0.3%
51800 458
 
0.3%
52200 453
 
0.3%
52000 452
 
0.3%
58500 450
 
0.3%
50600 449
 
0.3%
Other values (16231) 130075
96.5%
ValueCountFrequency (%)
10500 1
< 0.1%
10600 1
< 0.1%
11800 1
< 0.1%
12600 1
< 0.1%
12800 1
< 0.1%
13100 1
< 0.1%
13900 1
< 0.1%
14000 1
< 0.1%
14300 2
< 0.1%
14400 1
< 0.1%
ValueCountFrequency (%)
3526021 1
< 0.1%
2170551 1
< 0.1%
1876239 1
< 0.1%
1845118 1
< 0.1%
1666057 1
< 0.1%
1622113 1
< 0.1%
1190600 1
< 0.1%
1182139 1
< 0.1%
1180887 1
< 0.1%
1174400 1
< 0.1%

Data speed
Real number (ℝ)

High correlation 

Distinct606
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.746598
Minimum0
Maximum2043
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-03-09T14:33:52.681804image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49
Q174
median83
Q3100
95-th percentile152
Maximum2043
Range2043
Interquartile range (IQR)26

Descriptive statistics

Standard deviation47.330834
Coefficient of variation (CV)0.51588653
Kurtosis95.496904
Mean91.746598
Median Absolute Deviation (MAD)11
Skewness5.9656636
Sum12364322
Variance2240.2079
MonotonicityNot monotonic
2025-03-09T14:33:52.771709image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 4291
 
3.2%
74 4258
 
3.2%
73 4161
 
3.1%
75 3881
 
2.9%
76 3864
 
2.9%
77 3644
 
2.7%
78 3504
 
2.6%
79 3306
 
2.5%
80 3255
 
2.4%
81 3108
 
2.3%
Other values (596) 97494
72.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 60
 
< 0.1%
3 296
 
0.2%
4 607
0.5%
5 428
0.3%
6 251
 
0.2%
7 909
0.7%
8 326
 
0.2%
9 179
 
0.1%
ValueCountFrequency (%)
2043 1
< 0.1%
2040 1
< 0.1%
1505 1
< 0.1%
1496 1
< 0.1%
1406 1
< 0.1%
1172 1
< 0.1%
1144 1
< 0.1%
987 1
< 0.1%
950 1
< 0.1%
934 1
< 0.1%

Avg packet len
Real number (ℝ)

Zeros 

Distinct1269
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean903.03977
Minimum0
Maximum1518
Zeros38076
Zeros (%)28.3%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-03-09T14:33:52.859355image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1262
Q31381
95-th percentile1504
Maximum1518
Range1518
Interquartile range (IQR)1381

Descriptive statistics

Standard deviation608.96898
Coefficient of variation (CV)0.67435455
Kurtosis-1.3551282
Mean903.03977
Median Absolute Deviation (MAD)224
Skewness-0.66736297
Sum1.2169906 × 108
Variance370843.22
MonotonicityNot monotonic
2025-03-09T14:33:52.946417image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38076
28.3%
1028 6229
 
4.6%
1506 5050
 
3.7%
780 4470
 
3.3%
1296 2948
 
2.2%
1475 2536
 
1.9%
1278 2084
 
1.5%
1458 1256
 
0.9%
1478 1056
 
0.8%
1339 993
 
0.7%
Other values (1259) 70068
52.0%
ValueCountFrequency (%)
0 38076
28.3%
10 1
 
< 0.1%
24 1
 
< 0.1%
47 1
 
< 0.1%
50 1
 
< 0.1%
51 1
 
< 0.1%
52 1
 
< 0.1%
57 1
 
< 0.1%
63 2
 
< 0.1%
65 3
 
< 0.1%
ValueCountFrequency (%)
1518 606
0.4%
1517 101
 
0.1%
1516 34
 
< 0.1%
1515 40
 
< 0.1%
1514 34
 
< 0.1%
1513 31
 
< 0.1%
1512 145
 
0.1%
1511 8
 
< 0.1%
1510 189
 
0.1%
1509 15
 
< 0.1%

Avg source IP count
Real number (ℝ)

High correlation  Skewed 

Distinct315
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.47394
Minimum0
Maximum7374
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-03-09T14:33:53.029003image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile12
Maximum7374
Range7374
Interquartile range (IQR)2

Descriptive statistics

Standard deviation41.07175
Coefficient of variation (CV)9.1802192
Kurtosis13117.956
Mean4.47394
Median Absolute Deviation (MAD)0
Skewness98.67006
Sum602935
Variance1686.8887
MonotonicityNot monotonic
2025-03-09T14:33:53.115783image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 84733
62.9%
2 10529
 
7.8%
3 8088
 
6.0%
4 6925
 
5.1%
5 5630
 
4.2%
6 4248
 
3.2%
7 3079
 
2.3%
8 1963
 
1.5%
9 1210
 
0.9%
10 873
 
0.6%
Other values (305) 7488
 
5.6%
ValueCountFrequency (%)
0 7
 
< 0.1%
1 84733
62.9%
2 10529
 
7.8%
3 8088
 
6.0%
4 6925
 
5.1%
5 5630
 
4.2%
6 4248
 
3.2%
7 3079
 
2.3%
8 1963
 
1.5%
9 1210
 
0.9%
ValueCountFrequency (%)
7374 1
< 0.1%
5266 1
< 0.1%
4131 1
< 0.1%
3952 1
< 0.1%
3894 1
< 0.1%
3822 1
< 0.1%
3733 1
< 0.1%
2640 1
< 0.1%
2329 1
< 0.1%
2281 1
< 0.1%
Distinct127981
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Minimum2022-08-08 18:09:36
Maximum2023-04-27 12:32:29
Invalid dates0
Invalid dates (%)0.0%
2025-03-09T14:33:53.209864image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:53.296083image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct127917
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Minimum2022-08-08 18:09:37
Maximum2023-04-27 12:32:32
Invalid dates0
Invalid dates (%)0.0%
2025-03-09T14:33:53.377230image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:53.462948image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Type
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Normal traffic
124133 
Suspicious traffic
 
9707
DDoS attack
 
926

Length

Max length18
Median length14
Mean length14.267501
Min length11

Characters and Unicode

Total characters1922774
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal traffic
2nd rowNormal traffic
3rd rowNormal traffic
4th rowNormal traffic
5th rowNormal traffic

Common Values

ValueCountFrequency (%)
Normal traffic 124133
92.1%
Suspicious traffic 9707
 
7.2%
DDoS attack 926
 
0.7%

Length

2025-03-09T14:33:53.544532image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:53.613735image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
traffic 133840
49.7%
normal 124133
46.1%
suspicious 9707
 
3.6%
ddos 926
 
0.3%
attack 926
 
0.3%

Most occurring characters

ValueCountFrequency (%)
f 267680
13.9%
a 259825
13.5%
r 257973
13.4%
i 153254
8.0%
c 144473
7.5%
t 135692
7.1%
134766
7.0%
o 134766
7.0%
N 124133
6.5%
l 124133
6.5%
Other values (7) 186079
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1922774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 267680
13.9%
a 259825
13.5%
r 257973
13.4%
i 153254
8.0%
c 144473
7.5%
t 135692
7.1%
134766
7.0%
o 134766
7.0%
N 124133
6.5%
l 124133
6.5%
Other values (7) 186079
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1922774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 267680
13.9%
a 259825
13.5%
r 257973
13.4%
i 153254
8.0%
c 144473
7.5%
t 135692
7.1%
134766
7.0%
o 134766
7.0%
N 124133
6.5%
l 124133
6.5%
Other values (7) 186079
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1922774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 267680
13.9%
a 259825
13.5%
r 257973
13.4%
i 153254
8.0%
c 144473
7.5%
t 135692
7.1%
134766
7.0%
o 134766
7.0%
N 124133
6.5%
l 124133
6.5%
Other values (7) 186079
9.7%

Duration
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct1375
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.398409
Minimum0
Maximum41009
Zeros19644
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2025-03-09T14:33:53.685326image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q35
95-th percentile115
Maximum41009
Range41009
Interquartile range (IQR)4

Descriptive statistics

Standard deviation213.92108
Coefficient of variation (CV)7.037246
Kurtosis10838.225
Mean30.398409
Median Absolute Deviation (MAD)1
Skewness70.569197
Sum4096672
Variance45762.23
MonotonicityNot monotonic
2025-03-09T14:33:53.768345image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 60980
45.2%
0 19644
 
14.6%
3 8251
 
6.1%
2 6884
 
5.1%
4 4765
 
3.5%
5 1202
 
0.9%
6 868
 
0.6%
11 834
 
0.6%
7 744
 
0.6%
8 741
 
0.5%
Other values (1365) 29853
22.2%
ValueCountFrequency (%)
0 19644
 
14.6%
1 60980
45.2%
2 6884
 
5.1%
3 8251
 
6.1%
4 4765
 
3.5%
5 1202
 
0.9%
6 868
 
0.6%
7 744
 
0.6%
8 741
 
0.5%
9 724
 
0.5%
ValueCountFrequency (%)
41009 1
< 0.1%
17388 1
< 0.1%
12275 1
< 0.1%
11857 1
< 0.1%
11344 1
< 0.1%
11064 1
< 0.1%
10211 1
< 0.1%
9633 1
< 0.1%
9581 1
< 0.1%
8807 1
< 0.1%

ACK Attack
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134763 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134763
> 99.9%
1 3
 
< 0.1%

Length

2025-03-09T14:33:53.845343image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:53.903375image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134763
> 99.9%
1 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 134763
> 99.9%
1 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134763
> 99.9%
1 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134763
> 99.9%
1 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134763
> 99.9%
1 3
 
< 0.1%

CHARGEN
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134764 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134764
> 99.9%
1 2
 
< 0.1%

Length

2025-03-09T14:33:53.969026image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:54.030059image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134764
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 134764
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134764
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134764
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134764
> 99.9%
1 2
 
< 0.1%

CLDAP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134684 
1
 
82

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134684
99.9%
1 82
 
0.1%

Length

2025-03-09T14:33:54.095050image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:54.153943image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134684
99.9%
1 82
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 134684
99.9%
1 82
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134684
99.9%
1 82
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134684
99.9%
1 82
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134684
99.9%
1 82
 
0.1%

CoAP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134755 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Length

2025-03-09T14:33:54.217095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:54.274865image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

DNS
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134590 
1
 
176

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134590
99.9%
1 176
 
0.1%

Length

2025-03-09T14:33:54.337429image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:54.394882image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134590
99.9%
1 176
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 134590
99.9%
1 176
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134590
99.9%
1 176
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134590
99.9%
1 176
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134590
99.9%
1 176
 
0.1%

Generic UDP
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134454 
1
 
312

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134454
99.8%
1 312
 
0.2%

Length

2025-03-09T14:33:54.457823image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:54.515519image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134454
99.8%
1 312
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 134454
99.8%
1 312
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134454
99.8%
1 312
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134454
99.8%
1 312
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134454
99.8%
1 312
 
0.2%

High volume traffic
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
127485 
0
 
7281

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 127485
94.6%
0 7281
 
5.4%

Length

2025-03-09T14:33:54.578194image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:54.635795image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 127485
94.6%
0 7281
 
5.4%

Most occurring characters

ValueCountFrequency (%)
1 127485
94.6%
0 7281
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 127485
94.6%
0 7281
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 127485
94.6%
0 7281
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 127485
94.6%
0 7281
 
5.4%

IPv4 fragmentation
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134743 
1
 
23

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134743
> 99.9%
1 23
 
< 0.1%

Length

2025-03-09T14:33:54.698341image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:54.756906image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134743
> 99.9%
1 23
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 134743
> 99.9%
1 23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134743
> 99.9%
1 23
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134743
> 99.9%
1 23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134743
> 99.9%
1 23
 
< 0.1%

NTP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134695 
1
 
71

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134695
99.9%
1 71
 
0.1%

Length

2025-03-09T14:33:54.818146image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:54.876575image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134695
99.9%
1 71
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 134695
99.9%
1 71
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134695
99.9%
1 71
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134695
99.9%
1 71
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134695
99.9%
1 71
 
0.1%

RDP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134765 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Length

2025-03-09T14:33:54.938482image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:54.996753image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

RPC
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134765 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Length

2025-03-09T14:33:55.058286image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:55.116724image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

SNMP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134765 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Length

2025-03-09T14:33:55.178581image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:55.237597image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134765
> 99.9%
1 1
 
< 0.1%

SSDP
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134757 
1
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134757
> 99.9%
1 9
 
< 0.1%

Length

2025-03-09T14:33:55.299330image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:55.358312image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134757
> 99.9%
1 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 134757
> 99.9%
1 9
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134757
> 99.9%
1 9
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134757
> 99.9%
1 9
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134757
> 99.9%
1 9
 
< 0.1%

SYN Attack
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134518 
1
 
248

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134518
99.8%
1 248
 
0.2%

Length

2025-03-09T14:33:55.420104image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:55.479393image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134518
99.8%
1 248
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 134518
99.8%
1 248
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134518
99.8%
1 248
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134518
99.8%
1 248
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134518
99.8%
1 248
 
0.2%

Sentinel
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134761 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134761
> 99.9%
1 5
 
< 0.1%

Length

2025-03-09T14:33:55.541408image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:55.599999image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134761
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 134761
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134761
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134761
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134761
> 99.9%
1 5
 
< 0.1%

Suspicious traffic
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
124878 
1
 
9888

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124878
92.7%
1 9888
 
7.3%

Length

2025-03-09T14:33:55.663315image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:55.721286image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 124878
92.7%
1 9888
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 124878
92.7%
1 9888
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 124878
92.7%
1 9888
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 124878
92.7%
1 9888
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 124878
92.7%
1 9888
 
7.3%

TCP Anomaly
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
0
134755 
1
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters134766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Length

2025-03-09T14:33:55.784624image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-09T14:33:55.843255image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 134766
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 134755
> 99.9%
1 11
 
< 0.1%

Interactions

2025-03-09T14:33:50.241274image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.244764image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.838566image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.410862image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.960310image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.541470image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.106450image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.673000image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:50.307855image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.319570image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.910260image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.487575image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.036005image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.611951image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.178715image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.746863image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:50.373575image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.387179image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.981652image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.554483image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.103790image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.680508image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.246064image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.817246image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:50.439662image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.454196image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.052587image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.618450image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.173713image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.746809image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.315707image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.886653image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:50.507715image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.524296image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.133102image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.688565image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.246486image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.818454image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.388612image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.962150image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:50.575777image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.595549image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.204317image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.758353image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.327800image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.891551image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.460774image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:50.037029image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:50.642329image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.672588image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.276862image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.828088image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.399596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.966133image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.537405image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:50.108140image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:50.706889image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:46.766648image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.347795image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:47.897691image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:48.477657image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.039808image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:49.608471image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-03-09T14:33:50.178521image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-03-09T14:33:55.907339image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ACK AttackAttack IDAvg packet lenAvg source IP countCHARGENCLDAPCoAPDNSData speedDetect countDurationGeneric UDPHigh volume trafficIPv4 fragmentationNTPPacket speedPort numberRDPRPCSNMPSSDPSYN AttackSentinelSuspicious trafficTCP AnomalyType
ACK Attack1.0000.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0120.0000.0000.0000.0000.0000.0000.0000.4350.057
Attack ID0.0121.000-0.0110.1200.0080.0390.0090.038-0.222-0.0160.1940.0460.2760.0140.025-0.1300.0390.0000.0000.0000.0120.1020.0050.3420.0260.246
Avg packet len0.000-0.0111.0000.1090.0260.0660.0280.034-0.1130.0050.0140.1240.2290.0160.156-0.2190.0140.0300.0300.0160.1400.0660.0610.2920.0110.221
Avg source IP count0.0000.1200.1091.0000.4740.0000.0000.294-0.1630.0280.0740.0270.0260.5770.0950.0060.2820.4470.0000.4470.1490.0310.0000.0340.0000.095
CHARGEN0.0000.0080.0260.4741.0000.0000.0000.0800.5120.0000.0000.0000.0120.2210.1260.3920.0000.0000.0000.0000.0000.0000.0000.0100.0000.046
CLDAP0.0000.0390.0660.0000.0001.0000.0000.0200.0000.2050.1190.0000.0250.0000.0000.0060.0260.0000.0000.0000.0000.0000.0000.0030.0000.297
CoAP0.0000.0090.0280.0000.0000.0001.0000.0000.0000.1510.1060.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.109
DNS0.0000.0380.0340.2940.0800.0200.0001.0000.1930.0330.0000.0090.1180.1490.0300.1600.0240.0380.0000.0380.0000.0000.0000.0180.0000.435
Data speed0.000-0.222-0.113-0.1630.5120.0000.0000.1931.0000.062-0.0180.0590.0310.5200.1250.7930.0160.0000.0190.1540.0000.0000.0000.0440.0000.075
Detect count0.000-0.0160.0050.0280.0000.2050.1510.0330.0621.0000.7920.0610.0000.0000.0000.152-0.0170.0000.0000.0000.0000.0000.0000.0270.0000.067
Duration0.0000.1940.0140.0740.0000.1190.1060.000-0.0180.7921.0000.1060.0120.0000.0000.0440.0160.0000.0000.0000.0000.0000.0000.0300.0000.061
Generic UDP0.0000.0460.1240.0270.0000.0000.0000.0090.0590.0610.1061.0000.1380.0050.0000.0580.0400.0000.0280.0000.0000.0000.0000.0530.0000.579
High volume traffic0.0160.2760.2290.0260.0120.0250.0210.1180.0310.0000.0120.1381.0000.0310.0650.0310.1550.0050.0050.0000.0200.1780.0230.7750.0360.817
IPv4 fragmentation0.0000.0140.0160.5770.2210.0000.0000.1490.5200.0000.0000.0050.0311.0000.0370.4040.0120.0000.0000.1040.0000.0050.0000.0170.0000.157
NTP0.0000.0250.1560.0950.1260.0000.0000.0300.1250.0000.0000.0000.0650.0371.0000.2200.0220.0590.0000.0000.0190.0000.0000.0230.0000.276
Packet speed0.000-0.130-0.2190.0060.3920.0060.0000.1600.7930.1520.0440.0580.0310.4040.2201.0000.0680.0890.0890.4080.0920.0000.0000.0400.0000.085
Port number0.0120.0390.0140.2820.0000.0260.0000.0240.016-0.0170.0160.0400.1550.0120.0220.0681.0000.0230.0000.0000.0140.0450.0000.2180.0190.163
RDP0.0000.0000.0300.4470.0000.0000.0000.0380.0000.0000.0000.0000.0050.0000.0590.0890.0231.0000.0000.0000.0000.0000.0000.0000.0000.033
RPC0.0000.0000.0300.0000.0000.0000.0000.0000.0190.0000.0000.0280.0050.0000.0000.0890.0000.0001.0000.0000.0000.0000.0000.0000.0000.033
SNMP0.0000.0000.0160.4470.0000.0000.0000.0380.1540.0000.0000.0000.0000.1040.0000.4080.0000.0000.0001.0000.0000.0000.0000.0040.0000.033
SSDP0.0000.0120.1400.1490.0000.0000.0000.0000.0000.0000.0000.0000.0200.0000.0190.0920.0140.0000.0000.0001.0000.0000.0000.0060.0000.098
SYN Attack0.0000.1020.0660.0310.0000.0000.0000.0000.0000.0000.0000.0000.1780.0050.0000.0000.0450.0000.0000.0000.0001.0000.0000.0110.0000.516
Sentinel0.0000.0050.0610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.073
Suspicious traffic0.0000.3420.2920.0340.0100.0030.0000.0180.0440.0270.0300.0530.7750.0170.0230.0400.2180.0000.0000.0040.0060.0110.0001.0000.0000.992
TCP Anomaly0.4350.0260.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0360.0000.0000.0000.0190.0000.0000.0000.0000.0000.0000.0001.0000.109
Type0.0570.2460.2210.0950.0460.2970.1090.4350.0750.0670.0610.5790.8170.1570.2760.0850.1630.0330.0330.0330.0980.5160.0730.9920.1091.000

Missing values

2025-03-09T14:33:50.828259image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-09T14:33:51.209211image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Attack IDVictim IPPort numberAttack codeDetect countPacket speedData speedAvg packet lenAvg source IP countStart timeEnd timeTypeDurationACK AttackCHARGENCLDAPCoAPDNSGeneric UDPHigh volume trafficIPv4 fragmentationNTPRDPRPCSNMPSSDPSYN AttackSentinelSuspicious trafficTCP Anomaly
01IP_00014500[High volume traffic]15560073138362022-08-08 18:09:362022-08-08 18:09:37Normal traffic1.000000010000000000
12IP_00024500[High volume traffic]16350090150612022-08-08 18:37:282022-08-08 18:37:28Normal traffic0.000000010000000000
23IP_00031200[High volume traffic]26270082139912022-08-08 18:41:252022-08-08 18:41:26Normal traffic1.000000010000000000
34IP_00024500[High volume traffic]15950085148612022-08-08 18:47:492022-08-08 18:47:50Normal traffic1.000000010000000000
45IP_000412347[High volume traffic]278250113151812022-08-08 18:57:152022-08-08 18:58:11Normal traffic56.000000010000000000
56IP_000545574[Suspicious traffic]1891002124612022-08-08 19:09:292022-08-08 19:09:29Suspicious traffic0.000000000000000010
67IP_00014500[High volume traffic]26825090139532022-08-08 19:11:362022-08-08 19:11:37Normal traffic1.000000010000000000
78IP_00031200[High volume traffic]178500104139912022-08-08 19:17:322022-08-08 19:17:33Normal traffic1.000000010000000000
89IP_00014500[High volume traffic]16440086140442022-08-08 19:28:362022-08-08 19:28:37Normal traffic1.000000010000000000
910IP_000660645[High volume traffic]15500077146612022-08-08 19:30:052022-08-08 19:30:05Normal traffic0.000000010000000000
Attack IDVictim IPPort numberAttack codeDetect countPacket speedData speedAvg packet lenAvg source IP countStart timeEnd timeTypeDurationACK AttackCHARGENCLDAPCoAPDNSGeneric UDPHigh volume trafficIPv4 fragmentationNTPRDPRPCSNMPSSDPSYN AttackSentinelSuspicious trafficTCP Anomaly
134756134757IP_00231685[High volume traffic]15680068122822023-04-27 12:20:522023-04-27 12:20:55Normal traffic3.000000010000000000
134757134758IP_140570[High volume traffic]15010071148212023-04-27 12:22:322023-04-27 12:22:35Normal traffic3.000000010000000000
134758134759IP_001551821[High volume traffic]25130068138912023-04-27 12:23:002023-04-27 12:25:07Normal traffic127.000000010000000000
134759134760IP_180920[High volume traffic]27670094129812023-04-27 12:23:502023-04-27 12:23:54Normal traffic4.000000010000000000
134760134761IP_004061281[High volume traffic]45920172128752023-04-27 12:25:392023-04-27 12:30:34Normal traffic295.000000010000000000
134761134762IP_168160[High volume traffic]256400691294252023-04-27 12:28:172023-04-27 12:31:08Normal traffic171.000000010000000000
134762134763IP_00030[High volume traffic]15350074146222023-04-27 12:30:182023-04-27 12:30:21Normal traffic3.000000010000000000
134764134765IP_002348529[High volume traffic]26655076122132023-04-27 12:31:102023-04-27 12:32:32Normal traffic82.000000010000000000
134765134766IP_178270[High volume traffic]15220074150612023-04-27 12:31:202023-04-27 12:31:23Normal traffic3.000000010000000000
134766134767IP_174660[High volume traffic]15130064132512023-04-27 12:32:292023-04-27 12:32:32Normal traffic3.000000010000000000